Fitting very large sparse Gaussian graphical models

In this paper we consider some methods for the maximum likelihood estimation of sparse Gaussian graphical (covariance selection) models when the number of variables is very large (tens of thousands or more). We present a procedure for determining the pattern of zeros in themodel andwe discuss the use of limitedmemory quasi-Newton algorithms and… CONTINUE READING